This invention relates to detecting data bit patterns reproduced from a magnetic storage medium and more particularly, to a neural network in the read channel of a magnetic disk drive.
Conventional pulse detectors used in disk drives convert the pre-amplified and equalized analog read head pulses into digital pulses. Two criteria are applied. The first criterion is that the individual pulse be above a certain amplitude, the "gating threshold". This is intended to avoid detection of lower level noise pulses. Once the first criterion is satisfied, the second criterion is that the first derivative of the pre-amplified and equalized analog read head pulses passes through zero, i.e., detection is at the actual peak of the signal.
If the pulses are crowded together to achieve higher density they mutually interfere with neighboring pulses. The gating threshold criterion may not be satisfied. This threshold must be lowered to the "new" threshold. However, this has the undesirable characteristic of being closer to the noise so that noise rejection suffers. In addition, the peaks of the pulses are pushed away from each other, making their detected position incorrect. Further, the nonlinearities inherent in the recording and playback process at points of high mutual pulse interference, called "intersymbol interference" or "ISI", cannot be compensated for by a linear equalizer. The purpose of the equalizer is to provide the corrective frequency response to cause closely-spaced pulses to be amplified more than widely spaced pulses. Linear equalizers can only correct for linear interference of pulses, not non-linear interferences. Such linear equalizers inherently emphasize higher frequency components, causing a degradation of the wideband signal-to-noise ratio, or "SNR".
Artificial neural systems (ANS) automatically learn to recognize categories of features based on experience. The approach is based on the simulation of biological systems of nerve cells. Each cell is called a "neuron" and systems of neurons are called "neural systems" or "neural networks". Neural networks are often simulated using large systems of ordinary differential equations where the response of a single neuron to inputs is governed by a single differential equation. See U.S. Pat. No. 4,906,940.
Algorithms for "training" these neural networks are well known and include the MADELINE and the back propagation procedures. See, for example, IEEE International Conference on Neural Networks, Sheraton Harbor Island, San Diego, CALIF. July, 24-27, 1988, MADELINE RULE II, by Winter & Widrow, pp. I-401 through I-408 and BASIC MECHANISMS, pp. 320-362.
Recently, electronically trainable analog neural networks have become available. See the data sheet for the Intel Corporation, 80170NX, "Electronically Trainable Analog Neural Network", (Experimental), Intel Order No. 290408-002.
It is an object of the present invention to use a neural network to recognize data bit patterns in the read channel of a magnetic disk or tape drive, when data is written at previously unachievably high densities along a track, exacerbating Intersymbol Interference and non-linear effects.